The first presented approach is based on random walks [1]}. The main idea behind the algorithm is that for each graph node \(u\) , we learn how to predict \(u\) 's occurrence from its context. In natural language the context of each word is the set of surrounding words in a sequence, and for graph nodes the context is the set of local neighbors. Various algorithms have been proposed that allow some flexibility in selecting local neighbors according to different criteria, such as LINE [2]}, PTE [3]}, node2vec [4]}, APP [5]} and VERSE [6]}.
